Overview

Dataset statistics

Number of variables25
Number of observations10000
Missing cells10812
Missing cells (%)4.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.3 MiB
Average record size in memory655.5 B

Variable types

Categorical9
Numeric16

Alerts

Customer_ID has a high cardinality: 7126 distinct valuesHigh cardinality
Type_of_Loan has a high cardinality: 3890 distinct valuesHigh cardinality
Credit_History_Age has a high cardinality: 402 distinct valuesHigh cardinality
Annual_Income is highly overall correlated with Monthly_Inhand_Salary and 2 other fieldsHigh correlation
Monthly_Inhand_Salary is highly overall correlated with Annual_Income and 2 other fieldsHigh correlation
Num_Bank_Accounts is highly overall correlated with Interest_Rate and 3 other fieldsHigh correlation
Interest_Rate is highly overall correlated with Num_Bank_Accounts and 4 other fieldsHigh correlation
Num_of_Loan is highly overall correlated with Outstanding_DebtHigh correlation
Delay_from_due_date is highly overall correlated with Num_Bank_Accounts and 6 other fieldsHigh correlation
Num_of_Delayed_Payment is highly overall correlated with Num_Bank_Accounts and 2 other fieldsHigh correlation
Changed_Credit_Limit is highly overall correlated with Payment_of_Min_AmountHigh correlation
Num_Credit_Inquiries is highly overall correlated with Num_Bank_Accounts and 3 other fieldsHigh correlation
Outstanding_Debt is highly overall correlated with Interest_Rate and 5 other fieldsHigh correlation
Amount_invested_monthly is highly overall correlated with Annual_Income and 1 other fieldsHigh correlation
Monthly_Balance is highly overall correlated with Annual_Income and 1 other fieldsHigh correlation
Credit_Mix is highly overall correlated with Delay_from_due_date and 2 other fieldsHigh correlation
Payment_of_Min_Amount is highly overall correlated with Delay_from_due_date and 3 other fieldsHigh correlation
Age has 463 (4.6%) missing valuesMissing
Annual_Income has 727 (7.3%) missing valuesMissing
Monthly_Inhand_Salary has 1507 (15.1%) missing valuesMissing
Num_of_Loan has 463 (4.6%) missing valuesMissing
Type_of_Loan has 1060 (10.6%) missing valuesMissing
Num_of_Delayed_Payment has 1012 (10.1%) missing valuesMissing
Changed_Credit_Limit has 243 (2.4%) missing valuesMissing
Num_Credit_Inquiries has 178 (1.8%) missing valuesMissing
Credit_Mix has 2005 (20.1%) missing valuesMissing
Outstanding_Debt has 105 (1.1%) missing valuesMissing
Credit_History_Age has 886 (8.9%) missing valuesMissing
Payment_of_Min_Amount has 1176 (11.8%) missing valuesMissing
Amount_invested_monthly has 868 (8.7%) missing valuesMissing
Monthly_Balance has 119 (1.2%) missing valuesMissing
Customer_ID is uniformly distributedUniform
Credit_Utilization_Ratio has unique valuesUnique
Num_Bank_Accounts has 409 (4.1%) zerosZeros
Num_of_Loan has 962 (9.6%) zerosZeros
Num_of_Delayed_Payment has 142 (1.4%) zerosZeros
Num_Credit_Inquiries has 699 (7.0%) zerosZeros
Total_EMI_per_month has 982 (9.8%) zerosZeros

Reproduction

Analysis started2023-03-14 15:15:02.966465
Analysis finished2023-03-14 15:15:46.650326
Duration43.68 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Customer_ID
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct7126
Distinct (%)71.3%
Missing0
Missing (%)0.0%
Memory size731.9 KiB
CUS_0x8b31
 
5
CUS_0xaec2
 
5
CUS_0x53eb
 
5
CUS_0x54bd
 
4
CUS_0x65aa
 
4
Other values (7121)
9977 

Length

Max length10
Median length10
Mean length9.942
Min length9

Characters and Unicode

Total characters99420
Distinct characters21
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4802 ?
Unique (%)48.0%

Sample

1st rowCUS_0x65fa
2nd rowCUS_0x8927
3rd rowCUS_0xbe6f
4th rowCUS_0x3de6
5th rowCUS_0x64aa

Common Values

ValueCountFrequency (%)
CUS_0x8b31 5
 
0.1%
CUS_0xaec2 5
 
0.1%
CUS_0x53eb 5
 
0.1%
CUS_0x54bd 4
 
< 0.1%
CUS_0x65aa 4
 
< 0.1%
CUS_0x9a55 4
 
< 0.1%
CUS_0x5ed7 4
 
< 0.1%
CUS_0x35c6 4
 
< 0.1%
CUS_0xbda9 4
 
< 0.1%
CUS_0x946d 4
 
< 0.1%
Other values (7116) 9957
99.6%

Length

2023-03-14T16:15:46.715219image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cus_0x8b31 5
 
< 0.1%
cus_0x53eb 5
 
< 0.1%
cus_0xaec2 5
 
< 0.1%
cus_0x54bd 4
 
< 0.1%
cus_0x748e 4
 
< 0.1%
cus_0x8d9 4
 
< 0.1%
cus_0x5a59 4
 
< 0.1%
cus_0x1fd1 4
 
< 0.1%
cus_0x2771 4
 
< 0.1%
cus_0xc203 4
 
< 0.1%
Other values (7116) 9957
99.6%

Most occurring characters

ValueCountFrequency (%)
0 11895
12.0%
C 10000
 
10.1%
S 10000
 
10.1%
_ 10000
 
10.1%
x 10000
 
10.1%
U 10000
 
10.1%
4 2820
 
2.8%
5 2771
 
2.8%
6 2728
 
2.7%
3 2725
 
2.7%
Other values (11) 26481
26.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 36084
36.3%
Uppercase Letter 30000
30.2%
Lowercase Letter 23336
23.5%
Connector Punctuation 10000
 
10.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11895
33.0%
4 2820
 
7.8%
5 2771
 
7.7%
6 2728
 
7.6%
3 2725
 
7.6%
9 2674
 
7.4%
8 2644
 
7.3%
7 2618
 
7.3%
1 2615
 
7.2%
2 2594
 
7.2%
Lowercase Letter
ValueCountFrequency (%)
x 10000
42.9%
b 2706
 
11.6%
a 2671
 
11.4%
c 2311
 
9.9%
d 1931
 
8.3%
e 1896
 
8.1%
f 1821
 
7.8%
Uppercase Letter
ValueCountFrequency (%)
C 10000
33.3%
S 10000
33.3%
U 10000
33.3%
Connector Punctuation
ValueCountFrequency (%)
_ 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 53336
53.6%
Common 46084
46.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11895
25.8%
_ 10000
21.7%
4 2820
 
6.1%
5 2771
 
6.0%
6 2728
 
5.9%
3 2725
 
5.9%
9 2674
 
5.8%
8 2644
 
5.7%
7 2618
 
5.7%
1 2615
 
5.7%
Latin
ValueCountFrequency (%)
C 10000
18.7%
S 10000
18.7%
x 10000
18.7%
U 10000
18.7%
b 2706
 
5.1%
a 2671
 
5.0%
c 2311
 
4.3%
d 1931
 
3.6%
e 1896
 
3.6%
f 1821
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 99420
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11895
12.0%
C 10000
 
10.1%
S 10000
 
10.1%
_ 10000
 
10.1%
x 10000
 
10.1%
U 10000
 
10.1%
4 2820
 
2.8%
5 2771
 
2.8%
6 2728
 
2.7%
3 2725
 
2.7%
Other values (11) 26481
26.6%

Month
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size686.0 KiB
August
1282 
January
1267 
March
1262 
July
1259 
June
1251 
Other values (3)
3679 

Length

Max length8
Median length6
Mean length5.249
Min length3

Characters and Unicode

Total characters52490
Distinct characters19
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMarch
2nd rowFebruary
3rd rowApril
4th rowJanuary
5th rowApril

Common Values

ValueCountFrequency (%)
August 1282
12.8%
January 1267
12.7%
March 1262
12.6%
July 1259
12.6%
June 1251
12.5%
May 1244
12.4%
February 1224
12.2%
April 1211
12.1%

Length

2023-03-14T16:15:46.823529image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-14T16:15:46.963078image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
august 1282
12.8%
january 1267
12.7%
march 1262
12.6%
july 1259
12.6%
june 1251
12.5%
may 1244
12.4%
february 1224
12.2%
april 1211
12.1%

Most occurring characters

ValueCountFrequency (%)
u 7565
14.4%
a 6264
11.9%
r 6188
11.8%
y 4994
9.5%
J 3777
 
7.2%
n 2518
 
4.8%
M 2506
 
4.8%
A 2493
 
4.7%
e 2475
 
4.7%
l 2470
 
4.7%
Other values (9) 11240
21.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 42490
80.9%
Uppercase Letter 10000
 
19.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 7565
17.8%
a 6264
14.7%
r 6188
14.6%
y 4994
11.8%
n 2518
 
5.9%
e 2475
 
5.8%
l 2470
 
5.8%
t 1282
 
3.0%
s 1282
 
3.0%
g 1282
 
3.0%
Other values (5) 6170
14.5%
Uppercase Letter
ValueCountFrequency (%)
J 3777
37.8%
M 2506
25.1%
A 2493
24.9%
F 1224
 
12.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 52490
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
u 7565
14.4%
a 6264
11.9%
r 6188
11.8%
y 4994
9.5%
J 3777
 
7.2%
n 2518
 
4.8%
M 2506
 
4.8%
A 2493
 
4.7%
e 2475
 
4.7%
l 2470
 
4.7%
Other values (9) 11240
21.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 52490
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
u 7565
14.4%
a 6264
11.9%
r 6188
11.8%
y 4994
9.5%
J 3777
 
7.2%
n 2518
 
4.8%
M 2506
 
4.8%
A 2493
 
4.7%
e 2475
 
4.7%
l 2470
 
4.7%
Other values (9) 11240
21.4%

Age
Real number (ℝ)

Distinct218
Distinct (%)2.3%
Missing463
Missing (%)4.6%
Infinite0
Infinite (%)0.0%
Mean111.93258
Minimum-500
Maximum8505
Zeros0
Zeros (%)0.0%
Negative81
Negative (%)0.8%
Memory size156.2 KiB
2023-03-14T16:15:47.109137image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-500
5-th percentile16
Q124
median33
Q342
95-th percentile53
Maximum8505
Range9005
Interquartile range (IQR)18

Descriptive statistics

Standard deviation684.93214
Coefficient of variation (CV)6.1191492
Kurtosis85.965915
Mean111.93258
Median Absolute Deviation (MAD)9
Skewness9.0246588
Sum1067501
Variance469132.04
MonotonicityNot monotonic
2023-03-14T16:15:47.228839image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 301
 
3.0%
38 301
 
3.0%
26 297
 
3.0%
31 291
 
2.9%
43 284
 
2.8%
19 278
 
2.8%
20 278
 
2.8%
21 277
 
2.8%
42 276
 
2.8%
28 274
 
2.7%
Other values (208) 6680
66.8%
(Missing) 463
 
4.6%
ValueCountFrequency (%)
-500 81
 
0.8%
14 111
 
1.1%
15 170
1.7%
16 118
1.2%
17 155
1.6%
18 217
2.2%
19 278
2.8%
20 278
2.8%
21 277
2.8%
22 255
2.5%
ValueCountFrequency (%)
8505 1
< 0.1%
8470 1
< 0.1%
8425 1
< 0.1%
8406 1
< 0.1%
8352 1
< 0.1%
8315 1
< 0.1%
8233 1
< 0.1%
8216 1
< 0.1%
8154 1
< 0.1%
8149 1
< 0.1%

Occupation
Categorical

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size717.2 KiB
_______
691 
Manager
682 
Accountant
674 
Lawyer
665 
Mechanic
 
655
Other values (11)
6633 

Length

Max length13
Median length10
Mean length8.4395
Min length6

Characters and Unicode

Total characters84395
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowArchitect
2nd rowMedia_Manager
3rd rowJournalist
4th rowEntrepreneur
5th rowJournalist

Common Values

ValueCountFrequency (%)
_______ 691
 
6.9%
Manager 682
 
6.8%
Accountant 674
 
6.7%
Lawyer 665
 
6.7%
Mechanic 655
 
6.6%
Media_Manager 653
 
6.5%
Architect 643
 
6.4%
Developer 640
 
6.4%
Engineer 624
 
6.2%
Scientist 620
 
6.2%
Other values (6) 3453
34.5%

Length

2023-03-14T16:15:47.351699image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
691
 
6.9%
manager 682
 
6.8%
accountant 674
 
6.7%
lawyer 665
 
6.7%
mechanic 655
 
6.6%
media_manager 653
 
6.5%
architect 643
 
6.4%
developer 640
 
6.4%
engineer 624
 
6.2%
scientist 620
 
6.2%
Other values (6) 3453
34.5%

Most occurring characters

ValueCountFrequency (%)
e 11289
13.4%
r 8593
10.2%
n 7494
 
8.9%
a 7009
 
8.3%
c 6281
 
7.4%
t 6199
 
7.3%
i 6024
 
7.1%
_ 5490
 
6.5%
M 3177
 
3.8%
o 3056
 
3.6%
Other values (18) 19783
23.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 68943
81.7%
Uppercase Letter 9962
 
11.8%
Connector Punctuation 5490
 
6.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 11289
16.4%
r 8593
12.5%
n 7494
10.9%
a 7009
10.2%
c 6281
9.1%
t 6199
9.0%
i 6024
8.7%
o 3056
 
4.4%
u 2367
 
3.4%
g 1959
 
2.8%
Other values (8) 8672
12.6%
Uppercase Letter
ValueCountFrequency (%)
M 3177
31.9%
A 1317
13.2%
D 1229
 
12.3%
E 1219
 
12.2%
L 665
 
6.7%
S 620
 
6.2%
T 594
 
6.0%
W 577
 
5.8%
J 564
 
5.7%
Connector Punctuation
ValueCountFrequency (%)
_ 5490
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 78905
93.5%
Common 5490
 
6.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 11289
14.3%
r 8593
10.9%
n 7494
9.5%
a 7009
8.9%
c 6281
 
8.0%
t 6199
 
7.9%
i 6024
 
7.6%
M 3177
 
4.0%
o 3056
 
3.9%
u 2367
 
3.0%
Other values (17) 17416
22.1%
Common
ValueCountFrequency (%)
_ 5490
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84395
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 11289
13.4%
r 8593
10.2%
n 7494
 
8.9%
a 7009
 
8.3%
c 6281
 
7.4%
t 6199
 
7.3%
i 6024
 
7.1%
_ 5490
 
6.5%
M 3177
 
3.8%
o 3056
 
3.6%
Other values (18) 19783
23.4%

Annual_Income
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6809
Distinct (%)73.4%
Missing727
Missing (%)7.3%
Infinite0
Infinite (%)0.0%
Mean187407.65
Minimum7006.035
Maximum24198062
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2023-03-14T16:15:47.466224image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum7006.035
5-th percentile9897.565
Q119536.45
median37071.2
Q372287.52
95-th percentile135743.29
Maximum24198062
Range24191056
Interquartile range (IQR)52751.07

Descriptive statistics

Standard deviation1480977.2
Coefficient of variation (CV)7.9024371
Kurtosis151.22029
Mean187407.65
Median Absolute Deviation (MAD)21002.81
Skewness11.967588
Sum1.7378312 × 109
Variance2.1932934 × 1012
MonotonicityNot monotonic
2023-03-14T16:15:47.598075image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17445.15 5
 
0.1%
40341.16 5
 
0.1%
94811.36 5
 
0.1%
10120.28 4
 
< 0.1%
98019.84 4
 
< 0.1%
26309.54 4
 
< 0.1%
14155.74 4
 
< 0.1%
19227.17 4
 
< 0.1%
10439.97 4
 
< 0.1%
136150.6 4
 
< 0.1%
Other values (6799) 9230
92.3%
(Missing) 727
 
7.3%
ValueCountFrequency (%)
7006.035 3
< 0.1%
7011.685 1
 
< 0.1%
7012.31 2
< 0.1%
7020.545 1
 
< 0.1%
7046.5 1
 
< 0.1%
7056.405 1
 
< 0.1%
7059.455 1
 
< 0.1%
7064.385 1
 
< 0.1%
7077.87 1
 
< 0.1%
7079.32 1
 
< 0.1%
ValueCountFrequency (%)
24198062 1
< 0.1%
23884555 1
< 0.1%
23784659 1
< 0.1%
23498432 1
< 0.1%
23266988 1
< 0.1%
22853346 1
< 0.1%
22644332 1
< 0.1%
22456326 1
< 0.1%
22063213 1
< 0.1%
21958247 1
< 0.1%

Monthly_Inhand_Salary
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6426
Distinct (%)75.7%
Missing1507
Missing (%)15.1%
Infinite0
Infinite (%)0.0%
Mean4171.7644
Minimum332.12833
Maximum15204.633
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2023-03-14T16:15:47.730291image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum332.12833
5-th percentile850.57133
Q11626.7617
median3079.355
Q35954.6467
95-th percentile10854.38
Maximum15204.633
Range14872.505
Interquartile range (IQR)4327.885

Descriptive statistics

Standard deviation3173.6832
Coefficient of variation (CV)0.76075322
Kurtosis0.67007997
Mean4171.7644
Median Absolute Deviation (MAD)1727.9225
Skewness1.1530678
Sum35430795
Variance10072265
MonotonicityNot monotonic
2023-03-14T16:15:47.848602image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10739.36667 5
 
0.1%
840.06 4
 
< 0.1%
1103.9975 4
 
< 0.1%
4753.526667 4
 
< 0.1%
11127.88333 4
 
< 0.1%
507.5954167 4
 
< 0.1%
1897.105 4
 
< 0.1%
10999.55333 4
 
< 0.1%
1463.2475 4
 
< 0.1%
2553.963333 4
 
< 0.1%
Other values (6416) 8452
84.5%
(Missing) 1507
 
15.1%
ValueCountFrequency (%)
332.1283333 2
< 0.1%
332.43125 2
< 0.1%
333.5966667 1
< 0.1%
355.2083333 1
< 0.1%
357.2558333 2
< 0.1%
368.3741667 1
< 0.1%
379.3908333 1
< 0.1%
393.1591667 1
< 0.1%
393.69875 1
< 0.1%
403.2541667 1
< 0.1%
ValueCountFrequency (%)
15204.63333 1
< 0.1%
15167.18 2
< 0.1%
15136.69667 1
< 0.1%
15101.94 1
< 0.1%
15038.31667 1
< 0.1%
14958.33667 1
< 0.1%
14880.38333 1
< 0.1%
14867.81333 1
< 0.1%
14866.44667 2
< 0.1%
14856.48333 1
< 0.1%

Num_Bank_Accounts
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct151
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.6387
Minimum-1
Maximum1777
Zeros409
Zeros (%)4.1%
Negative2
Negative (%)< 0.1%
Memory size156.2 KiB
2023-03-14T16:15:47.980582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q14
median6
Q38
95-th percentile10
Maximum1777
Range1778
Interquartile range (IQR)4

Descriptive statistics

Standard deviation117.78515
Coefficient of variation (CV)6.6776551
Kurtosis124.57827
Mean17.6387
Median Absolute Deviation (MAD)2
Skewness10.840897
Sum176387
Variance13873.343
MonotonicityNot monotonic
2023-03-14T16:15:48.096792image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 1294
12.9%
6 1293
12.9%
7 1287
12.9%
5 1232
12.3%
4 1190
11.9%
3 1187
11.9%
9 560
5.6%
10 549
5.5%
2 433
 
4.3%
1 423
 
4.2%
Other values (141) 552
5.5%
ValueCountFrequency (%)
-1 2
 
< 0.1%
0 409
 
4.1%
1 423
 
4.2%
2 433
 
4.3%
3 1187
11.9%
4 1190
11.9%
5 1232
12.3%
6 1293
12.9%
7 1287
12.9%
8 1294
12.9%
ValueCountFrequency (%)
1777 1
< 0.1%
1763 1
< 0.1%
1756 1
< 0.1%
1739 1
< 0.1%
1735 1
< 0.1%
1714 1
< 0.1%
1652 1
< 0.1%
1645 1
< 0.1%
1637 1
< 0.1%
1631 1
< 0.1%

Num_Credit_Card
Real number (ℝ)

Distinct213
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.6755
Minimum0
Maximum1490
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2023-03-14T16:15:48.224078image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q14
median6
Q37
95-th percentile10
Maximum1490
Range1490
Interquartile range (IQR)3

Descriptive statistics

Standard deviation120.18454
Coefficient of variation (CV)5.8128962
Kurtosis83.661014
Mean20.6755
Median Absolute Deviation (MAD)1
Skewness8.9335599
Sum206755
Variance14444.323
MonotonicityNot monotonic
2023-03-14T16:15:48.346168image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 1845
18.4%
6 1705
17.1%
7 1648
16.5%
4 1337
13.4%
3 1318
13.2%
8 529
 
5.3%
10 494
 
4.9%
9 458
 
4.6%
1 236
 
2.4%
2 207
 
2.1%
Other values (203) 223
 
2.2%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 236
 
2.4%
2 207
 
2.1%
3 1318
13.2%
4 1337
13.4%
5 1845
18.4%
6 1705
17.1%
7 1648
16.5%
8 529
 
5.3%
9 458
 
4.6%
ValueCountFrequency (%)
1490 2
< 0.1%
1480 1
< 0.1%
1477 1
< 0.1%
1473 1
< 0.1%
1466 1
< 0.1%
1440 1
< 0.1%
1436 1
< 0.1%
1426 1
< 0.1%
1420 1
< 0.1%
1407 1
< 0.1%

Interest_Rate
Real number (ℝ)

Distinct216
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.6847
Minimum1
Maximum5797
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2023-03-14T16:15:48.500831image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q18
median14
Q320
95-th percentile32
Maximum5797
Range5796
Interquartile range (IQR)12

Descriptive statistics

Standard deviation442.44066
Coefficient of variation (CV)6.5367899
Kurtosis93.795239
Mean67.6847
Median Absolute Deviation (MAD)6
Skewness9.4052611
Sum676847
Variance195753.74
MonotonicityNot monotonic
2023-03-14T16:15:48.652168image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 520
 
5.2%
5 498
 
5.0%
6 465
 
4.7%
12 458
 
4.6%
9 450
 
4.5%
10 446
 
4.5%
15 425
 
4.2%
11 421
 
4.2%
7 416
 
4.2%
18 415
 
4.2%
Other values (206) 5486
54.9%
ValueCountFrequency (%)
1 269
2.7%
2 224
2.2%
3 273
2.7%
4 288
2.9%
5 498
5.0%
6 465
4.7%
7 416
4.2%
8 520
5.2%
9 450
4.5%
10 446
4.5%
ValueCountFrequency (%)
5797 1
< 0.1%
5776 1
< 0.1%
5770 1
< 0.1%
5745 1
< 0.1%
5729 1
< 0.1%
5698 1
< 0.1%
5677 1
< 0.1%
5633 1
< 0.1%
5623 1
< 0.1%
5615 1
< 0.1%

Num_of_Loan
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct51
Distinct (%)0.5%
Missing463
Missing (%)4.6%
Infinite0
Infinite (%)0.0%
Mean2.5174583
Minimum-100
Maximum1482
Zeros962
Zeros (%)9.6%
Negative391
Negative (%)3.9%
Memory size156.2 KiB
2023-03-14T16:15:48.861102image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile0
Q12
median3
Q35
95-th percentile8
Maximum1482
Range1582
Interquartile range (IQR)3

Descriptive statistics

Standard deviation59.970722
Coefficient of variation (CV)23.821933
Kurtosis336.90301
Mean2.5174583
Median Absolute Deviation (MAD)2
Skewness16.461218
Sum24009
Variance3596.4876
MonotonicityNot monotonic
2023-03-14T16:15:49.042424image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 1423
14.2%
2 1421
14.2%
4 1406
14.1%
1 1017
10.2%
0 962
9.6%
7 783
7.8%
6 749
7.5%
5 659
6.6%
-100 391
 
3.9%
9 376
 
3.8%
Other values (41) 350
 
3.5%
(Missing) 463
 
4.6%
ValueCountFrequency (%)
-100 391
 
3.9%
0 962
9.6%
1 1017
10.2%
2 1421
14.2%
3 1423
14.2%
4 1406
14.1%
5 659
6.6%
6 749
7.5%
7 783
7.8%
8 309
 
3.1%
ValueCountFrequency (%)
1482 1
< 0.1%
1480 1
< 0.1%
1465 1
< 0.1%
1406 1
< 0.1%
1391 1
< 0.1%
1354 1
< 0.1%
1319 1
< 0.1%
1150 2
< 0.1%
1129 1
< 0.1%
1017 1
< 0.1%

Type_of_Loan
Categorical

HIGH CARDINALITY  MISSING 

Distinct3890
Distinct (%)43.5%
Missing1060
Missing (%)10.6%
Memory size1.2 MiB
Debt Consolidation Loan
 
135
Personal Loan
 
134
Not Specified
 
132
Student Loan
 
127
Credit-Builder Loan
 
126
Other values (3885)
8286 

Length

Max length178
Median length139
Mean length67.350671
Min length9

Characters and Unicode

Total characters602115
Distinct characters33
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2265 ?
Unique (%)25.3%

Sample

1st rowStudent Loan, and Personal Loan
2nd rowStudent Loan, Home Equity Loan, and Payday Loan
3rd rowMortgage Loan, Auto Loan, Not Specified, Not Specified, and Debt Consolidation Loan
4th rowHome Equity Loan, Personal Loan, Debt Consolidation Loan, and Debt Consolidation Loan
5th rowCredit-Builder Loan, and Not Specified

Common Values

ValueCountFrequency (%)
Debt Consolidation Loan 135
 
1.4%
Personal Loan 134
 
1.3%
Not Specified 132
 
1.3%
Student Loan 127
 
1.3%
Credit-Builder Loan 126
 
1.3%
Home Equity Loan 121
 
1.2%
Auto Loan 117
 
1.2%
Mortgage Loan 112
 
1.1%
Payday Loan 111
 
1.1%
Payday Loan, and Auto Loan 31
 
0.3%
Other values (3880) 7794
77.9%
(Missing) 1060
 
10.6%

Length

2023-03-14T16:15:49.229162image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
loan 32081
36.5%
and 7825
 
8.9%
payday 4195
 
4.8%
credit-builder 4050
 
4.6%
home 4050
 
4.6%
equity 4050
 
4.6%
personal 4019
 
4.6%
debt 4001
 
4.6%
consolidation 4001
 
4.6%
not 3935
 
4.5%
Other values (4) 15701
17.9%

Most occurring characters

ValueCountFrequency (%)
78968
13.1%
o 63923
10.6%
a 60230
 
10.0%
n 55858
 
9.3%
e 35885
 
6.0%
t 35734
 
5.9%
L 32081
 
5.3%
d 31987
 
5.3%
i 28022
 
4.7%
, 27076
 
4.5%
Other values (23) 152351
25.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 407888
67.7%
Uppercase Letter 84133
 
14.0%
Space Separator 78968
 
13.1%
Other Punctuation 27076
 
4.5%
Dash Punctuation 4050
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 63923
15.7%
a 60230
14.8%
n 55858
13.7%
e 35885
8.8%
t 35734
8.8%
d 31987
7.8%
i 28022
6.9%
r 16033
 
3.9%
u 15952
 
3.9%
y 12440
 
3.0%
Other values (9) 51824
12.7%
Uppercase Letter
ValueCountFrequency (%)
L 32081
38.1%
P 8214
 
9.8%
C 8051
 
9.6%
S 7866
 
9.3%
E 4050
 
4.8%
B 4050
 
4.8%
H 4050
 
4.8%
D 4001
 
4.8%
N 3935
 
4.7%
A 3921
 
4.7%
Space Separator
ValueCountFrequency (%)
78968
100.0%
Other Punctuation
ValueCountFrequency (%)
, 27076
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4050
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 492021
81.7%
Common 110094
 
18.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 63923
13.0%
a 60230
12.2%
n 55858
11.4%
e 35885
 
7.3%
t 35734
 
7.3%
L 32081
 
6.5%
d 31987
 
6.5%
i 28022
 
5.7%
r 16033
 
3.3%
u 15952
 
3.2%
Other values (20) 116316
23.6%
Common
ValueCountFrequency (%)
78968
71.7%
, 27076
 
24.6%
- 4050
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 602115
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
78968
13.1%
o 63923
10.6%
a 60230
 
10.0%
n 55858
 
9.3%
e 35885
 
6.0%
t 35734
 
5.9%
L 32081
 
5.3%
d 31987
 
5.3%
i 28022
 
4.7%
, 27076
 
4.5%
Other values (23) 152351
25.3%

Delay_from_due_date
Real number (ℝ)

Distinct73
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.2693
Minimum-5
Maximum67
Zeros88
Zeros (%)0.9%
Negative53
Negative (%)0.5%
Memory size156.2 KiB
2023-03-14T16:15:49.389847image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-5
5-th percentile3
Q110
median18
Q328
95-th percentile54
Maximum67
Range72
Interquartile range (IQR)18

Descriptive statistics

Standard deviation14.95075
Coefficient of variation (CV)0.70292629
Kurtosis0.28179097
Mean21.2693
Median Absolute Deviation (MAD)9
Skewness0.94353487
Sum212693
Variance223.52493
MonotonicityNot monotonic
2023-03-14T16:15:49.531253image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 335
 
3.4%
8 330
 
3.3%
14 329
 
3.3%
5 328
 
3.3%
9 325
 
3.2%
13 316
 
3.2%
12 312
 
3.1%
7 311
 
3.1%
10 310
 
3.1%
11 310
 
3.1%
Other values (63) 6794
67.9%
ValueCountFrequency (%)
-5 3
 
< 0.1%
-4 6
 
0.1%
-3 6
 
0.1%
-2 19
 
0.2%
-1 19
 
0.2%
0 88
0.9%
1 140
1.4%
2 138
1.4%
3 191
1.9%
4 182
1.8%
ValueCountFrequency (%)
67 7
 
0.1%
66 5
 
0.1%
65 4
 
< 0.1%
64 7
 
0.1%
63 7
 
0.1%
62 54
0.5%
61 52
0.5%
60 58
0.6%
59 37
0.4%
58 50
0.5%

Num_of_Delayed_Payment
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct97
Distinct (%)1.1%
Missing1012
Missing (%)10.1%
Infinite0
Infinite (%)0.0%
Mean29.833445
Minimum-3
Maximum4293
Zeros142
Zeros (%)1.4%
Negative65
Negative (%)0.7%
Memory size156.2 KiB
2023-03-14T16:15:49.694464image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3
5-th percentile2
Q19
median14
Q318
95-th percentile24
Maximum4293
Range4296
Interquartile range (IQR)9

Descriptive statistics

Standard deviation219.10876
Coefficient of variation (CV)7.3444002
Kurtosis227.20087
Mean29.833445
Median Absolute Deviation (MAD)5
Skewness14.69701
Sum268143
Variance48008.648
MonotonicityNot monotonic
2023-03-14T16:15:49.829566image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 561
 
5.6%
18 552
 
5.5%
19 539
 
5.4%
17 515
 
5.1%
16 512
 
5.1%
12 506
 
5.1%
15 494
 
4.9%
20 488
 
4.9%
9 468
 
4.7%
8 449
 
4.5%
Other values (87) 3904
39.0%
(Missing) 1012
 
10.1%
ValueCountFrequency (%)
-3 6
 
0.1%
-2 20
 
0.2%
-1 39
 
0.4%
0 142
1.4%
1 158
1.6%
2 186
1.9%
3 170
1.7%
4 186
1.9%
5 200
2.0%
6 237
2.4%
ValueCountFrequency (%)
4293 1
< 0.1%
4169 1
< 0.1%
4106 1
< 0.1%
4096 1
< 0.1%
4069 1
< 0.1%
4024 1
< 0.1%
3951 1
< 0.1%
3793 1
< 0.1%
3765 1
< 0.1%
3750 1
< 0.1%

Changed_Credit_Limit
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2706
Distinct (%)27.7%
Missing243
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean10.413096
Minimum-6.37
Maximum35.89
Zeros0
Zeros (%)0.0%
Negative169
Negative (%)1.7%
Memory size156.2 KiB
2023-03-14T16:15:50.233453image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-6.37
5-th percentile1.13
Q15.4
median9.45
Q314.9
95-th percentile23.37
Maximum35.89
Range42.26
Interquartile range (IQR)9.5

Descriptive statistics

Standard deviation6.7919433
Coefficient of variation (CV)0.65225013
Kurtosis0.11727852
Mean10.413096
Median Absolute Deviation (MAD)4.58
Skewness0.62990294
Sum101600.58
Variance46.130494
MonotonicityNot monotonic
2023-03-14T16:15:50.357159image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.49 19
 
0.2%
11.5 18
 
0.2%
0.57 17
 
0.2%
8.22 16
 
0.2%
10.7 16
 
0.2%
9.97 16
 
0.2%
14.37 15
 
0.1%
8.3 14
 
0.1%
10.31 14
 
0.1%
8.74 13
 
0.1%
Other values (2696) 9599
96.0%
(Missing) 243
 
2.4%
ValueCountFrequency (%)
-6.37 1
< 0.1%
-6.35 1
< 0.1%
-6.27 1
< 0.1%
-6.02 1
< 0.1%
-5.9 1
< 0.1%
-5.63 1
< 0.1%
-5.55 1
< 0.1%
-5.52 1
< 0.1%
-5.01 2
< 0.1%
-4.97 1
< 0.1%
ValueCountFrequency (%)
35.89 1
< 0.1%
35.4 1
< 0.1%
35.02 1
< 0.1%
34.91 1
< 0.1%
34.81 1
< 0.1%
34.48 1
< 0.1%
33.98 1
< 0.1%
33.96 1
< 0.1%
33.92 1
< 0.1%
33.19 1
< 0.1%

Num_Credit_Inquiries
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct182
Distinct (%)1.9%
Missing178
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean29.019955
Minimum0
Maximum2568
Zeros699
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2023-03-14T16:15:50.508096image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median6
Q39
95-th percentile13
Maximum2568
Range2568
Interquartile range (IQR)6

Descriptive statistics

Standard deviation199.2386
Coefficient of variation (CV)6.8655721
Kurtosis93.242017
Mean29.019955
Median Absolute Deviation (MAD)3
Skewness9.4687865
Sum285034
Variance39696.018
MonotonicityNot monotonic
2023-03-14T16:15:50.662766image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 1069
10.7%
3 918
9.2%
8 832
8.3%
7 824
8.2%
6 791
 
7.9%
2 781
 
7.8%
1 743
 
7.4%
0 699
 
7.0%
9 564
 
5.6%
5 548
 
5.5%
Other values (172) 2053
20.5%
ValueCountFrequency (%)
0 699
7.0%
1 743
7.4%
2 781
7.8%
3 918
9.2%
4 1069
10.7%
5 548
5.5%
6 791
7.9%
7 824
8.2%
8 832
8.3%
9 564
5.6%
ValueCountFrequency (%)
2568 1
< 0.1%
2541 1
< 0.1%
2473 1
< 0.1%
2469 1
< 0.1%
2433 1
< 0.1%
2420 1
< 0.1%
2418 1
< 0.1%
2399 1
< 0.1%
2389 1
< 0.1%
2374 1
< 0.1%

Credit_Mix
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing2005
Missing (%)20.1%
Memory size624.9 KiB
1.0
3693 
2.0
2369 
0.0
1933 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters23985
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 3693
36.9%
2.0 2369
23.7%
0.0 1933
19.3%
(Missing) 2005
20.1%

Length

2023-03-14T16:15:50.789436image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-14T16:15:50.901371image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 3693
46.2%
2.0 2369
29.6%
0.0 1933
24.2%

Most occurring characters

ValueCountFrequency (%)
0 9928
41.4%
. 7995
33.3%
1 3693
 
15.4%
2 2369
 
9.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15990
66.7%
Other Punctuation 7995
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9928
62.1%
1 3693
 
23.1%
2 2369
 
14.8%
Other Punctuation
ValueCountFrequency (%)
. 7995
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23985
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9928
41.4%
. 7995
33.3%
1 3693
 
15.4%
2 2369
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23985
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9928
41.4%
. 7995
33.3%
1 3693
 
15.4%
2 2369
 
9.9%

Outstanding_Debt
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6986
Distinct (%)70.6%
Missing105
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean1433.7231
Minimum0.23
Maximum4998.07
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2023-03-14T16:15:51.030852image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.23
5-th percentile124.738
Q1562.34
median1175.67
Q31966.58
95-th percentile4106.36
Maximum4998.07
Range4997.84
Interquartile range (IQR)1404.24

Descriptive statistics

Standard deviation1162.1036
Coefficient of variation (CV)0.8105495
Kurtosis0.87663489
Mean1433.7231
Median Absolute Deviation (MAD)652.78
Skewness1.1988535
Sum14186690
Variance1350484.7
MonotonicityNot monotonic
2023-03-14T16:15:51.169643image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2362.17 5
 
0.1%
356.24 5
 
0.1%
461.05 5
 
0.1%
883.26 5
 
0.1%
1484.37 5
 
0.1%
55.2 5
 
0.1%
314.58 5
 
0.1%
255.76 5
 
0.1%
1636.23 4
 
< 0.1%
156.76 4
 
< 0.1%
Other values (6976) 9847
98.5%
(Missing) 105
 
1.1%
ValueCountFrequency (%)
0.23 2
< 0.1%
0.56 2
< 0.1%
0.95 2
< 0.1%
1.23 1
 
< 0.1%
1.33 1
 
< 0.1%
2.04 1
 
< 0.1%
2.13 1
 
< 0.1%
2.43 1
 
< 0.1%
3.31 3
< 0.1%
3.68 1
 
< 0.1%
ValueCountFrequency (%)
4998.07 1
< 0.1%
4992.25 1
< 0.1%
4990.91 1
< 0.1%
4987.19 1
< 0.1%
4986.03 1
< 0.1%
4984.82 1
< 0.1%
4980.31 1
< 0.1%
4974.31 1
< 0.1%
4973.64 2
< 0.1%
4972.4 1
< 0.1%

Credit_Utilization_Ratio
Real number (ℝ)

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.345498
Minimum21.273807
Maximum48.247003
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2023-03-14T16:15:51.297587image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum21.273807
5-th percentile24.267745
Q128.107137
median32.394472
Q336.59263
95-th percentile40.372347
Maximum48.247003
Range26.973196
Interquartile range (IQR)8.4854934

Descriptive statistics

Standard deviation5.1475369
Coefficient of variation (CV)0.1591423
Kurtosis-0.95611171
Mean32.345498
Median Absolute Deviation (MAD)4.2324135
Skewness0.024475673
Sum323454.98
Variance26.497136
MonotonicityNot monotonic
2023-03-14T16:15:51.437840image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.4882157 1
 
< 0.1%
29.68456247 1
 
< 0.1%
30.83446963 1
 
< 0.1%
26.60556424 1
 
< 0.1%
31.46398258 1
 
< 0.1%
35.05695098 1
 
< 0.1%
25.57489634 1
 
< 0.1%
31.57807818 1
 
< 0.1%
24.47333706 1
 
< 0.1%
36.84594811 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
21.27380667 1
< 0.1%
21.31090683 1
< 0.1%
21.50221669 1
< 0.1%
21.53227272 1
< 0.1%
21.61579547 1
< 0.1%
21.62851115 1
< 0.1%
21.7017408 1
< 0.1%
21.78319195 1
< 0.1%
21.80316585 1
< 0.1%
21.82161409 1
< 0.1%
ValueCountFrequency (%)
48.24700252 1
< 0.1%
48.19982398 1
< 0.1%
48.1917489 1
< 0.1%
48.02324923 1
< 0.1%
47.96956024 1
< 0.1%
46.51063306 1
< 0.1%
46.44557698 1
< 0.1%
45.41155254 1
< 0.1%
45.33520502 1
< 0.1%
45.2971679 1
< 0.1%

Credit_History_Age
Categorical

HIGH CARDINALITY  MISSING 

Distinct402
Distinct (%)4.4%
Missing886
Missing (%)8.9%
Memory size799.9 KiB
18 Years and 2 Months
 
51
15 Years and 9 Months
 
51
18 Years and 5 Months
 
49
15 Years and 11 Months
 
48
17 Years and 11 Months
 
48
Other values (397)
8867 

Length

Max length22
Median length21
Mean length20.982335
Min length20

Characters and Unicode

Total characters191233
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row32 Years and 10 Months
2nd row26 Years and 9 Months
3rd row10 Years and 10 Months
4th row20 Years and 7 Months
5th row20 Years and 4 Months

Common Values

ValueCountFrequency (%)
18 Years and 2 Months 51
 
0.5%
15 Years and 9 Months 51
 
0.5%
18 Years and 5 Months 49
 
0.5%
15 Years and 11 Months 48
 
0.5%
17 Years and 11 Months 48
 
0.5%
18 Years and 8 Months 47
 
0.5%
17 Years and 5 Months 47
 
0.5%
19 Years and 5 Months 46
 
0.5%
19 Years and 2 Months 45
 
0.4%
18 Years and 3 Months 45
 
0.4%
Other values (392) 8637
86.4%
(Missing) 886
 
8.9%

Length

2023-03-14T16:15:51.580375image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and 9114
20.0%
months 9114
20.0%
years 9114
20.0%
11 1102
 
2.4%
9 1092
 
2.4%
8 1081
 
2.4%
10 1026
 
2.3%
5 1008
 
2.2%
6 990
 
2.2%
7 959
 
2.1%
Other values (27) 10970
24.1%

Most occurring characters

ValueCountFrequency (%)
36456
19.1%
a 18228
9.5%
s 18228
9.5%
n 18228
9.5%
M 9114
 
4.8%
o 9114
 
4.8%
Y 9114
 
4.8%
e 9114
 
4.8%
r 9114
 
4.8%
d 9114
 
4.8%
Other values (12) 45409
23.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 109368
57.2%
Space Separator 36456
 
19.1%
Decimal Number 27181
 
14.2%
Uppercase Letter 18228
 
9.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7692
28.3%
2 4504
16.6%
3 2544
 
9.4%
0 2357
 
8.7%
9 1861
 
6.8%
8 1839
 
6.8%
7 1708
 
6.3%
6 1675
 
6.2%
5 1645
 
6.1%
4 1356
 
5.0%
Lowercase Letter
ValueCountFrequency (%)
a 18228
16.7%
s 18228
16.7%
n 18228
16.7%
o 9114
8.3%
e 9114
8.3%
r 9114
8.3%
d 9114
8.3%
h 9114
8.3%
t 9114
8.3%
Uppercase Letter
ValueCountFrequency (%)
M 9114
50.0%
Y 9114
50.0%
Space Separator
ValueCountFrequency (%)
36456
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 127596
66.7%
Common 63637
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
36456
57.3%
1 7692
 
12.1%
2 4504
 
7.1%
3 2544
 
4.0%
0 2357
 
3.7%
9 1861
 
2.9%
8 1839
 
2.9%
7 1708
 
2.7%
6 1675
 
2.6%
5 1645
 
2.6%
Latin
ValueCountFrequency (%)
a 18228
14.3%
s 18228
14.3%
n 18228
14.3%
M 9114
7.1%
o 9114
7.1%
Y 9114
7.1%
e 9114
7.1%
r 9114
7.1%
d 9114
7.1%
h 9114
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 191233
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
36456
19.1%
a 18228
9.5%
s 18228
9.5%
n 18228
9.5%
M 9114
 
4.8%
o 9114
 
4.8%
Y 9114
 
4.8%
e 9114
 
4.8%
r 9114
 
4.8%
d 9114
 
4.8%
Other values (12) 45409
23.7%

Payment_of_Min_Amount
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing1176
Missing (%)11.8%
Memory size641.1 KiB
1.0
5278 
0.0
3546 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters26472
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 5278
52.8%
0.0 3546
35.5%
(Missing) 1176
 
11.8%

Length

2023-03-14T16:15:51.699215image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-14T16:15:51.803984image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 5278
59.8%
0.0 3546
40.2%

Most occurring characters

ValueCountFrequency (%)
0 12370
46.7%
. 8824
33.3%
1 5278
19.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17648
66.7%
Other Punctuation 8824
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12370
70.1%
1 5278
29.9%
Other Punctuation
ValueCountFrequency (%)
. 8824
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 26472
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12370
46.7%
. 8824
33.3%
1 5278
19.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26472
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12370
46.7%
. 8824
33.3%
1 5278
19.9%

Total_EMI_per_month
Real number (ℝ)

Distinct6624
Distinct (%)66.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1453.6037
Minimum0
Maximum81194
Zeros982
Zeros (%)9.8%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2023-03-14T16:15:51.911469image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q131.496968
median71.274774
Q3164.6238
95-th percentile447.86809
Maximum81194
Range81194
Interquartile range (IQR)133.12683

Descriptive statistics

Standard deviation8401.0699
Coefficient of variation (CV)5.7794776
Kurtosis48.686218
Mean1453.6037
Median Absolute Deviation (MAD)50.488524
Skewness6.8769726
Sum14536037
Variance70577976
MonotonicityNot monotonic
2023-03-14T16:15:52.037899image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 982
 
9.8%
248.4607171 5
 
0.1%
27.14755195 4
 
< 0.1%
185.2959011 4
 
< 0.1%
94.57002245 4
 
< 0.1%
36.95983375 4
 
< 0.1%
39.6718144 4
 
< 0.1%
40.17366685 4
 
< 0.1%
135.2489551 4
 
< 0.1%
37.15545093 4
 
< 0.1%
Other values (6614) 8981
89.8%
ValueCountFrequency (%)
0 982
9.8%
4.713183572 2
 
< 0.1%
4.916138542 2
 
< 0.1%
5.262291048 1
 
< 0.1%
5.351086151 1
 
< 0.1%
5.463308978 2
 
< 0.1%
5.629824417 2
 
< 0.1%
5.905518076 1
 
< 0.1%
5.968634609 1
 
< 0.1%
5.994895587 1
 
< 0.1%
ValueCountFrequency (%)
81194 1
< 0.1%
80871 1
< 0.1%
80280 1
< 0.1%
80239 1
< 0.1%
80213 1
< 0.1%
80135 1
< 0.1%
80080 1
< 0.1%
79448 1
< 0.1%
79286 1
< 0.1%
79236 1
< 0.1%

Amount_invested_monthly
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9120
Distinct (%)99.9%
Missing868
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean194.64478
Minimum0
Maximum1890.8558
Zeros13
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2023-03-14T16:15:52.171773image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile32.064661
Q172.709619
median129.42891
Q3234.13252
95-th percentile611.29296
Maximum1890.8558
Range1890.8558
Interquartile range (IQR)161.4229

Descriptive statistics

Standard deviation199.32319
Coefficient of variation (CV)1.0240356
Kurtosis9.3592285
Mean194.64478
Median Absolute Deviation (MAD)67.961237
Skewness2.6394274
Sum1777496.1
Variance39729.733
MonotonicityNot monotonic
2023-03-14T16:15:52.290876image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13
 
0.1%
130.1580029 1
 
< 0.1%
135.642191 1
 
< 0.1%
20.37054805 1
 
< 0.1%
41.9750478 1
 
< 0.1%
31.92174979 1
 
< 0.1%
227.5540172 1
 
< 0.1%
171.0267804 1
 
< 0.1%
144.5797092 1
 
< 0.1%
47.01403903 1
 
< 0.1%
Other values (9110) 9110
91.1%
(Missing) 868
 
8.7%
ValueCountFrequency (%)
0 13
0.1%
10.07193677 1
 
< 0.1%
10.42191091 1
 
< 0.1%
10.54620674 1
 
< 0.1%
10.5717847 1
 
< 0.1%
10.73413246 1
 
< 0.1%
10.86864196 1
 
< 0.1%
10.98203275 1
 
< 0.1%
11.02466441 1
 
< 0.1%
11.2444098 1
 
< 0.1%
ValueCountFrequency (%)
1890.855773 1
< 0.1%
1804.355694 1
< 0.1%
1756.576983 1
< 0.1%
1701.055068 1
< 0.1%
1565.011244 1
< 0.1%
1523.290981 1
< 0.1%
1511.743094 1
< 0.1%
1490.985009 1
< 0.1%
1484.209196 1
< 0.1%
1461.679861 1
< 0.1%
Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size917.6 KiB
Low_spent_Small_value_payments
2615 
High_spent_Medium_value_payments
1726 
Low_spent_Medium_value_payments
1388 
High_spent_Large_value_payments
1339 
High_spent_Small_value_payments
1157 
Other values (2)
1775 

Length

Max length32
Median length31
Mean length28.9576
Min length6

Characters and Unicode

Total characters289576
Distinct characters29
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh_spent_Small_value_payments
2nd rowLow_spent_Small_value_payments
3rd rowHigh_spent_Small_value_payments
4th rowLow_spent_Small_value_payments
5th rowHigh_spent_Small_value_payments

Common Values

ValueCountFrequency (%)
Low_spent_Small_value_payments 2615
26.2%
High_spent_Medium_value_payments 1726
17.3%
Low_spent_Medium_value_payments 1388
13.9%
High_spent_Large_value_payments 1339
13.4%
High_spent_Small_value_payments 1157
11.6%
Low_spent_Large_value_payments 1035
 
10.3%
!@9#%8 740
 
7.4%

Length

2023-03-14T16:15:52.415391image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-14T16:15:52.542163image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
low_spent_small_value_payments 2615
26.2%
high_spent_medium_value_payments 1726
17.3%
low_spent_medium_value_payments 1388
13.9%
high_spent_large_value_payments 1339
13.4%
high_spent_small_value_payments 1157
11.6%
low_spent_large_value_payments 1035
 
10.3%
9#%8 740
 
7.4%

Most occurring characters

ValueCountFrequency (%)
_ 37040
12.8%
e 33268
11.5%
a 24666
 
8.5%
s 18520
 
6.4%
p 18520
 
6.4%
n 18520
 
6.4%
t 18520
 
6.4%
l 16804
 
5.8%
m 16146
 
5.6%
u 12374
 
4.3%
Other values (19) 75198
26.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 229576
79.3%
Connector Punctuation 37040
 
12.8%
Uppercase Letter 18520
 
6.4%
Other Punctuation 2960
 
1.0%
Decimal Number 1480
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 33268
14.5%
a 24666
10.7%
s 18520
8.1%
p 18520
8.1%
n 18520
8.1%
t 18520
8.1%
l 16804
 
7.3%
m 16146
 
7.0%
u 12374
 
5.4%
y 9260
 
4.0%
Other values (8) 42978
18.7%
Uppercase Letter
ValueCountFrequency (%)
L 7412
40.0%
H 4222
22.8%
S 3772
20.4%
M 3114
16.8%
Other Punctuation
ValueCountFrequency (%)
! 740
25.0%
@ 740
25.0%
# 740
25.0%
% 740
25.0%
Decimal Number
ValueCountFrequency (%)
9 740
50.0%
8 740
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 37040
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 248096
85.7%
Common 41480
 
14.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 33268
13.4%
a 24666
9.9%
s 18520
 
7.5%
p 18520
 
7.5%
n 18520
 
7.5%
t 18520
 
7.5%
l 16804
 
6.8%
m 16146
 
6.5%
u 12374
 
5.0%
y 9260
 
3.7%
Other values (12) 61498
24.8%
Common
ValueCountFrequency (%)
_ 37040
89.3%
! 740
 
1.8%
@ 740
 
1.8%
9 740
 
1.8%
# 740
 
1.8%
% 740
 
1.8%
8 740
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 289576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 37040
12.8%
e 33268
11.5%
a 24666
 
8.5%
s 18520
 
6.4%
p 18520
 
6.4%
n 18520
 
6.4%
t 18520
 
6.4%
l 16804
 
5.8%
m 16146
 
5.6%
u 12374
 
4.3%
Other values (19) 75198
26.0%

Monthly_Balance
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9881
Distinct (%)100.0%
Missing119
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean399.19647
Minimum0.90814584
Maximum1511.9166
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.2 KiB
2023-03-14T16:15:52.691973image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.90814584
5-th percentile172.4151
Q1268.6712
median334.52415
Q3465.51773
95-th percentile851.49579
Maximum1511.9166
Range1511.0085
Interquartile range (IQR)196.84652

Descriptive statistics

Standard deviation213.24553
Coefficient of variation (CV)0.53418691
Kurtosis3.2738453
Mean399.19647
Median Absolute Deviation (MAD)82.833186
Skewness1.6487557
Sum3944460.3
Variance45473.656
MonotonicityNot monotonic
2023-03-14T16:15:52.808522image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
339.7994878 1
 
< 0.1%
383.9356831 1
 
< 0.1%
441.2357318 1
 
< 0.1%
256.4158059 1
 
< 0.1%
100.1284199 1
 
< 0.1%
234.7622353 1
 
< 0.1%
329.6284117 1
 
< 0.1%
611.2733324 1
 
< 0.1%
269.4637068 1
 
< 0.1%
672.308116 1
 
< 0.1%
Other values (9871) 9871
98.7%
(Missing) 119
 
1.2%
ValueCountFrequency (%)
0.9081458437 1
< 0.1%
1.705492998 1
< 0.1%
1.780496225 1
< 0.1%
5.209196051 1
< 0.1%
5.238707186 1
< 0.1%
5.654871479 1
< 0.1%
6.673242297 1
< 0.1%
7.562479839 1
< 0.1%
7.828344202 1
< 0.1%
8.063863222 1
< 0.1%
ValueCountFrequency (%)
1511.91663 1
< 0.1%
1510.106671 1
< 0.1%
1507.553363 1
< 0.1%
1486.73702 1
< 0.1%
1454.718143 1
< 0.1%
1450.817184 1
< 0.1%
1446.819406 1
< 0.1%
1439.860707 1
< 0.1%
1422.567523 1
< 0.1%
1400.658458 1
< 0.1%

Credit_Score
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size644.5 KiB
1
5314 
0
2948 
2
1738 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 5314
53.1%
0 2948
29.5%
2 1738
 
17.4%

Length

2023-03-14T16:15:52.935592image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-14T16:15:53.034728image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1 5314
53.1%
0 2948
29.5%
2 1738
 
17.4%

Most occurring characters

ValueCountFrequency (%)
1 5314
53.1%
0 2948
29.5%
2 1738
 
17.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5314
53.1%
0 2948
29.5%
2 1738
 
17.4%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5314
53.1%
0 2948
29.5%
2 1738
 
17.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5314
53.1%
0 2948
29.5%
2 1738
 
17.4%

Interactions

2023-03-14T16:15:42.753816image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:06.837166image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:09.300599image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:11.830545image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:14.230055image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:16.513973image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:18.878017image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:21.157375image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:23.525533image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:25.908090image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:27.992563image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:30.221605image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:32.480331image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:34.721041image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:36.909901image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:39.510526image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:42.923598image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:06.989004image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:09.447602image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:11.968916image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:14.373284image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:16.637855image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:19.023991image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:21.285487image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:23.688220image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:26.026617image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:28.102238image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:30.348251image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:32.597408image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:34.844772image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:37.041544image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:39.663861image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:43.082874image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:07.135589image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:09.609162image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:12.112452image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:14.521965image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:16.882883image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:19.169943image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:21.426013image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:23.844113image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:26.155333image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:28.222820image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:30.481791image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:32.725073image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:34.970977image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:37.210529image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:39.812469image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:43.268104image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:07.290875image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:09.773292image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:12.261816image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:14.672632image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:17.021401image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:19.317075image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:21.568773image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:24.001857image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:26.286835image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:28.357943image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:30.620392image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:32.860986image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:35.105603image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:37.376894image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:39.971364image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:43.463923image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:07.461187image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:09.935730image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:12.413907image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:14.822317image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:17.163955image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:19.491249image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:21.710133image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:24.157624image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:26.419397image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:28.522212image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:30.915182image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:32.998024image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:35.242232image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:37.525419image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:40.164446image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:43.669845image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:07.611747image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:10.132696image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:12.556136image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:14.972098image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:17.301648image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:19.639821image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:21.851676image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:24.306498image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:26.551097image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:28.688480image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:31.047919image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:33.135516image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:35.407241image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:37.664452image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:40.324975image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:43.840979image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:07.749889image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:10.344881image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:12.691441image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:15.118718image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:17.436303image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:19.782139image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:21.985105image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:24.455607image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:26.680611image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:28.826801image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:31.174684image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:33.264995image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:35.592976image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:37.808724image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:40.755462image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:44.001312image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:07.893136image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:10.548624image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:12.831320image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:15.265085image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:17.571375image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:19.924744image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:22.126968image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:24.601112image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:26.813134image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:28.964060image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:31.312067image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:33.420420image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:35.742017image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:37.959515image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:40.938129image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:44.160411image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:08.034646image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:10.709711image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:12.972793image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:15.411833image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:17.709843image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:20.069816image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:22.266823image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:24.748636image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:26.951072image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:29.124250image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:31.450346image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:33.603816image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:35.877847image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:38.102093image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:41.144336image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:44.302984image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:08.158227image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:10.848432image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2023-03-14T16:15:15.543217image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:17.833377image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:20.195368image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2023-03-14T16:15:24.914864image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:27.085168image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:29.253585image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:31.571289image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:33.746344image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:36.000190image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:38.244064image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:41.348535image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:44.457556image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:08.333319image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:10.979320image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:13.226049image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:15.666656image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:17.952760image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:20.323832image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:22.511116image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:25.042936image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:27.202760image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:29.371931image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:31.688328image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:33.873338image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:36.121689image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:38.422220image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:41.513866image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:44.608734image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:08.503080image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:11.119950image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:13.380658image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:15.795403image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:18.085811image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:20.462906image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:22.649120image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:25.185190image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:27.328171image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:29.504364image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:31.811869image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:34.012818image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:36.249279image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:38.633675image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:41.722875image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:44.750011image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:08.667913image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:11.255267image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:13.549470image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:15.927270image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:18.216052image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:20.600171image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:22.780869image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:25.326095image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:27.463393image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:29.631301image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:31.932691image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:34.152637image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:36.378486image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:38.800232image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:41.919757image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:44.893194image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:08.814423image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:11.399006image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:13.703043image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:16.065127image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:18.373753image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:20.736103image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:23.042530image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:25.467339image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:27.589966image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:29.758994image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:32.058295image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:34.294740image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:36.503403image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:38.969903image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:42.135404image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:45.048512image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:08.973880image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:11.543435image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:13.868568image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:16.231439image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:18.558586image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:20.883436image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:23.188683image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:25.618246image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:27.731720image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:29.942198image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:32.199405image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:34.440601image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:36.650923image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:39.165891image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:42.363425image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:45.195074image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:09.126453image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:11.687619image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:14.039578image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:16.373127image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:18.724820image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:21.019073image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:23.343585image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:25.765160image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:27.862527image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:30.087498image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:32.336699image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:34.583459image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:36.781223image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:39.331923image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-03-14T16:15:42.570083image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2023-03-14T16:15:53.148036image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
AgeAnnual_IncomeMonthly_Inhand_SalaryNum_Bank_AccountsNum_Credit_CardInterest_RateNum_of_LoanDelay_from_due_dateNum_of_Delayed_PaymentChanged_Credit_LimitNum_Credit_InquiriesOutstanding_DebtCredit_Utilization_RatioTotal_EMI_per_monthAmount_invested_monthlyMonthly_BalanceMonthOccupationCredit_MixPayment_of_Min_AmountPayment_BehaviourCredit_Score
Age1.0000.0820.082-0.163-0.138-0.208-0.186-0.162-0.164-0.141-0.234-0.2010.026-0.0820.0510.1240.0080.0170.0050.0000.0000.000
Annual_Income0.0821.0000.975-0.260-0.189-0.278-0.217-0.236-0.240-0.139-0.258-0.2580.1470.4680.6220.5740.0000.0000.0220.0090.0160.011
Monthly_Inhand_Salary0.0820.9751.000-0.262-0.198-0.287-0.222-0.240-0.241-0.146-0.266-0.2720.1480.4610.6370.5970.0000.0280.2920.3500.1700.193
Num_Bank_Accounts-0.163-0.260-0.2621.0000.4090.5550.4130.5580.5560.2830.5030.485-0.0730.114-0.179-0.2970.0210.0000.0220.0160.0000.010
Num_Credit_Card-0.138-0.189-0.1980.4091.0000.4300.3480.4350.3790.1910.4020.438-0.0550.109-0.125-0.2320.0120.0060.0000.0000.0000.000
Interest_Rate-0.208-0.278-0.2870.5550.4301.0000.4720.5560.5450.3220.5840.590-0.0730.146-0.193-0.3400.0130.0110.0060.0000.0090.020
Num_of_Loan-0.186-0.217-0.2220.4130.3480.4721.0000.4260.4160.2800.4980.524-0.0950.489-0.152-0.4360.0080.0130.0030.0210.0120.008
Delay_from_due_date-0.162-0.236-0.2400.5580.4350.5560.4261.0000.5380.2730.5060.524-0.0720.144-0.158-0.3020.0140.0260.5740.5430.0360.339
Num_of_Delayed_Payment-0.164-0.240-0.2410.5560.3790.5450.4160.5381.0000.2670.4790.469-0.0640.135-0.173-0.2960.0160.0070.0250.0000.0000.014
Changed_Credit_Limit-0.141-0.139-0.1460.2830.1910.3220.2800.2730.2671.0000.3420.312-0.0460.114-0.099-0.1860.0110.0180.4340.5200.0130.168
Num_Credit_Inquiries-0.234-0.258-0.2660.5030.4020.5840.4980.5060.4790.3421.0000.581-0.0740.184-0.166-0.3400.0070.0070.0000.0000.0000.000
Outstanding_Debt-0.201-0.258-0.2720.4850.4380.5900.5240.5240.4690.3120.5811.000-0.0770.169-0.177-0.3560.0120.0320.5870.5690.0490.376
Credit_Utilization_Ratio0.0260.1470.148-0.073-0.055-0.073-0.095-0.072-0.064-0.046-0.074-0.0771.0000.0070.0250.2010.0000.0000.0980.1300.0760.040
Total_EMI_per_month-0.0820.4680.4610.1140.1090.1460.4890.1440.1350.1140.1840.1690.0071.0000.3010.0170.0000.0070.0000.0000.0250.027
Amount_invested_monthly0.0510.6220.637-0.179-0.125-0.193-0.152-0.158-0.173-0.099-0.166-0.1770.0250.3011.000-0.0370.0110.0000.1490.1920.1310.113
Monthly_Balance0.1240.5740.597-0.297-0.232-0.340-0.436-0.302-0.296-0.186-0.340-0.3560.2010.017-0.0371.0000.0110.0060.2850.3490.2430.171
Month0.0080.0000.0000.0210.0120.0130.0080.0140.0160.0110.0070.0120.0000.0000.0110.0111.0000.0040.0120.0000.0000.019
Occupation0.0170.0000.0280.0000.0060.0110.0130.0260.0070.0180.0070.0320.0000.0070.0000.0060.0041.0000.0170.0300.0000.028
Credit_Mix0.0050.0220.2920.0220.0000.0060.0030.5740.0250.4340.0000.5870.0980.0000.1490.2850.0120.0171.0000.8150.0850.452
Payment_of_Min_Amount0.0000.0090.3500.0160.0000.0000.0210.5430.0000.5200.0000.5690.1300.0000.1920.3490.0000.0300.8151.0000.1140.473
Payment_Behaviour0.0000.0160.1700.0000.0000.0090.0120.0360.0000.0130.0000.0490.0760.0250.1310.2430.0000.0000.0850.1141.0000.089
Credit_Score0.0000.0110.1930.0100.0000.0200.0080.3390.0140.1680.0000.3760.0400.0270.1130.1710.0190.0280.4520.4730.0891.000

Missing values

2023-03-14T16:15:45.461029image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-14T16:15:45.984392image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-03-14T16:15:46.414339image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Customer_IDMonthAgeOccupationAnnual_IncomeMonthly_Inhand_SalaryNum_Bank_AccountsNum_Credit_CardInterest_RateNum_of_LoanType_of_LoanDelay_from_due_dateNum_of_Delayed_PaymentChanged_Credit_LimitNum_Credit_InquiriesCredit_MixOutstanding_DebtCredit_Utilization_RatioCredit_History_AgePayment_of_Min_AmountTotal_EMI_per_monthAmount_invested_monthlyPayment_BehaviourMonthly_BalanceCredit_Score
65330CUS_0x65faMarchNaNArchitect123388.83010405.4025001432.0Student Loan, and Personal Loan2010.05.240.02.0869.6227.73865632 Years and 10 Months0.0114.305651365.741361High_spent_Small_value_payments820.4932381
15457CUS_0x8927February28.0Media_Manager29758.4602687.871667568-100.0Student Loan, Home Equity Loan, and Payday Loan2819.010.794.01.0817.4526.14852926 Years and 9 Months0.071.506030323.812292Low_spent_Small_value_payments163.4688441
51187CUS_0xbe6fApril40.0Journalist34945.1603043.09666776215.0Mortgage Loan, Auto Loan, Not Specified, Not Specified, and Debt Consolidation Loan2015.06.4512.01.0NaN40.47299410 Years and 10 Months1.0140.12097095.527792High_spent_Small_value_payments328.6609050
92784CUS_0x3de6January53.0Entrepreneur14475.1401092.26166783134.0Home Equity Loan, Personal Loan, Debt Consolidation Loan, and Debt Consolidation Loan2311.0-1.20NaN1.0609.6634.26023020 Years and 7 Months1.033.587273NaNLow_spent_Small_value_payments244.3678931
38283CUS_0x64aaApril20.0Journalist15961.5451269.12875052112.0Credit-Builder Loan, and Not Specified43.09.880.02.026.4228.93849420 Years and 4 Months0.021.248508NaNHigh_spent_Small_value_payments335.3932922
70344CUS_0x34c9January34.0Entrepreneur7200.005482.00041769178.0Payday Loan, Auto Loan, Mortgage Loan, Home Equity Loan, Home Equity Loan, Credit-Builder Loan, Mortgage Loan, and Home Equity Loan6118.02.546.00.02870.9229.0325413 Years and 1 Months1.042.78617918.023471High_spent_Small_value_payments247.3903921
36626CUS_0x30c6March31.0Musician68469.6005500.8000007633NaNAuto Loan, Student Loan, Mortgage Loan, Student Loan, and Home Equity Loan6219.05.167.0NaN1865.2824.473337NaN1.0230.392754138.842009High_spent_Small_value_payments440.8452370
53038CUS_0xc570July37.0Developer35851.3902762.6158336452.0Student Loan, and Auto Loan712.06.108.01.0926.9933.77882818 Years and 2 Months0.053.519095156.786374Low_spent_Large_value_payments335.9561141
80990CUS_0x9237July43.0Developer132387.52010844.2933333552.0Payday Loan, and Credit-Builder Loan210.08.952.02.0361.0840.15730829 Years and 9 Months0.0145.650758449.886734High_spent_Small_value_payments748.8918412
19209CUS_0x51e0FebruaryNaNLawyer30355.3502307.61250063124.0Home Equity Loan, Auto Loan, Personal Loan, and Mortgage Loan308.00.911.01.01328.4133.62399216 Years and 2 Months0.064.480944113.674564Low_spent_Medium_value_payments332.6057421
Customer_IDMonthAgeOccupationAnnual_IncomeMonthly_Inhand_SalaryNum_Bank_AccountsNum_Credit_CardInterest_RateNum_of_LoanType_of_LoanDelay_from_due_dateNum_of_Delayed_PaymentChanged_Credit_LimitNum_Credit_InquiriesCredit_MixOutstanding_DebtCredit_Utilization_RatioCredit_History_AgePayment_of_Min_AmountTotal_EMI_per_monthAmount_invested_monthlyPayment_BehaviourMonthly_BalanceCredit_Score
24652CUS_0xa6eMay24.0Musician82823.9207098.9933334518-100.0Payday Loan, and Not Specified29NaN10.597.01.0171.4536.13972213 Years and 0 Months1.0118.064832NaN!@9#%8634.3326251
30064CUS_0x1e52January23.0Media_Manager67059.1805398.26500086184.0Payday Loan, Debt Consolidation Loan, Debt Consolidation Loan, and Auto Loan1917.015.578.01.0255.7629.6152965 Years and 8 Months1.0167.123999160.057447High_spent_Medium_value_payments462.6450531
31581CUS_0x8da1June45.0_______15608.695NaN73124.0Auto Loan, Mortgage Loan, Debt Consolidation Loan, and Payday Loan719.012.3510.01.0488.3424.81199519 Years and 10 Months1.026.777829147.661238Low_spent_Small_value_payments252.2333921
50179CUS_0x1100April21.0Accountant43062.5403607.545000610232.0Mortgage Loan, and Student Loan5520.019.059.01.02169.7524.25929914 Years and 11 Months1.047.499819121.229734High_spent_Medium_value_payments442.0249470
51089CUS_0x663eFebruary38.0Musician18670.980NaN55144.0Debt Consolidation Loan, Debt Consolidation Loan, Payday Loan, and Payday Loan2517.015.927.01.01397.2135.60187010 Years and 5 Months1.032.444454199.960347Low_spent_Small_value_payments212.9866991
90966CUS_0x8843July37.0Developer11911.715917.64291756116.0Debt Consolidation Loan, Not Specified, Student Loan, Personal Loan, Not Specified, and Personal Loan2520.017.418.01.01147.4932.0917447 Years and 7 Months1.033.12245479.683939Low_spent_Small_value_payments268.9578991
91740CUS_0x94ddMay19.0Scientist23107.7401983.64500044101.0Debt Consolidation Loan720.012.457.01.0859.2335.29480628 Years and 1 Months1.017.603560197.124096Low_spent_Medium_value_payments263.6368451
98150CUS_0x85cbJuly22.0Writer68475.240NaN78325.0Not Specified, Home Equity Loan, Mortgage Loan, Debt Consolidation Loan, and Student Loan5124.09.2010.00.04116.6428.37037514 Years and 3 Months1.0236.00841939.416793High_spent_Large_value_payments513.4017881
46173CUS_0x510fJune50.0Writer39398.7403002.228333239NaNStudent Loan, Mortgage Loan, and Personal Loan112.06.386.02.062.3734.23780224 Years and 10 Months0.082.984233125.872776High_spent_Medium_value_payments341.3658251
28095CUS_0x3d8eAugust20.0Writer73026.9206233.5766672363.0Auto Loan, Debt Consolidation Loan, and Personal Loan713.04.434.02.098.7725.91518621 Years and 8 Months0.0158.959467423.806141Low_spent_Medium_value_payments320.5920581